Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.
We describe the way to get bene t from broad cultural trends through the quantitative analysis of a vast digital book collection representing the digested history of humanity. Our research work has revealed that appropriately comparing the occurrence patterns of words in some periods of human literature can help us to accurately determine the semantic similarity between these words by means of computers without requiring human intervention. Preliminary results seem to be promising.
Themis Palpanas and Kostas Stefanidis, ed. “Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference, EDBT/ICDT Workshops 2016, Bordeaux, France, March 15, 2016.” 2016.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.